This SBIR direct Phase II project is on developing a multimarker based technology for precisely selecting candidates for prophylactic treatments and preventing sporadic breast cancers. The patient population addressed here for selecting candidates is among subjects diagnosed with proliferative precancerous breast tumors that include atypias, papillomas, sclerosing adenosis and usual hyperplasias. Numerous clinical studies have established that diagnosis of above tumors is a risk factor and ~20-25% of atypical and 10-15% of non-atypical groups subsequently develop cancer in 1-5 or more years. Because of the increased risk, patients diagnosed with above tumors are offered standard prophylactic therapies such as Tamoxifen, Raloxifen, AIs or mastectomies. However, due to lack of any tools that precisely separate the 25%- 15% of high risk candidates, oncologists are faced with an uncertainty in precisely predicting cancer risk and dilemma in selecting candidates for the treatments. As a result, patients are either under-, or over-treated and those who have low risk are unnecessarily subjected to side effects of drugs or undergoing mastectomies. The high risk candidates who opt out to avoid side effects are not getting the benefit of prophylactic treatments. We have conducted a preliminary study to test the feasibility of stratifying the 25-15% high risk subjects by exploiting the differences in the biology of tumors from patients with and without the history of subsequent cancer development. We have derived ~300 putative markers that could predict cancer risk by gene expression approach and tested selected cancer markers in a limited number of tumor tissues. Our data demonstrated the feasibility of stratifying those who develop cancer from the low risk group based on cancer marker expressions. We have also shown the feasibility of translating multi-marker data into a ?Cancer Risk Score? for clinical utility. Our overall objective here is to develop a Multimarker Technology that will provide comprehensive information on whether 1) a subject will have the risk of developing cancer or low risk, 2) the future cancer will be ER+ or ER- and how long it may take to develop cancer so that oncologists can make an informed decision in selecting candidates for treatments. Our aims include 1) screening tissues for selective markers from the 300 to identify ~8-10 markers and develop algorithm that stratifies the high risk group with ?87 accuracy, 2) design a marker based algorithm that separates Cases into ER+ or ER- cancer risk, 3) validate the developed algorithms in new sets of samples, 4) investigate an inverse correlation between marker-derived risk scores and years to develop cancer after pre-cancer diagnosis and 5) finally develop a software program for implementing the technology. The broader impact of the planned Technology will be precision medicine in administering prophylactic treatments and sparing low risk patients from unnecessary side effects of drugs or mastectomies. The developed technology will also stratify high risk subjects into ER+ and ER- groups for administering preventive endocrine therapies. Finally the technology will become a standard tool in testing new drugs for preventing ER- cancers.